Subgroup biomarker identification: an information

Subgroup biomarker identification: an information theoretic insight Emily Turner and Konstantinos Sechidis, University of Manchester, Manchester Paul D. Metcalfe, Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Cambridge James Weatherall, Advanced Analytics Centre, Global Medicines Development, AstraZeneca, Alderley Park Gavin Brown, School of Computer Science, University of Manchester, Manchester Our work provides a theoretical and experimental comparison of three prominent methods for exploratory subgroup identification. We provide an information theoretic interpretation of the problem and connect it with the three methods. We believe that this interpretation brings additional clarity to the comparison. Our conclusions are that Virtual Twins (Foster et al. 2011) performs best by several measures. However, it appears to have weaknesses in distinguishing between predictive and prognostic biomarkers.